Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data
By Forrest R. Stevens, Andrea E. Gaughan, Catherine Linard, Andrew J. Tatem. PLoS ONE, 2015
985 citations recorded by the source corpus.
High resolution, contemporary data on human population distributions are vital for measuring impacts of population growth, monitoring human-environment interactions and for planning and policy development. Many methods are used to disaggregate census data and predict population densities for finer scale, gridded population data sets. We present a new semi-automated dasymetric modeling approach that incorporates detailed census and ancillary data in a flexible, “Random Forest” estimation technique. We outline the combination of widely available, remotely-sensed and geospatial data that contribute to the modeled dasymetric weights and then use the Random Forest model to generate a gridded prediction of population density at ~100 m spatial resolution. This prediction layer is then used as the weighting surface to perform dasymetric redistribution of the census counts at a country level. As a case study we compare the new algorithm and its products for three countries (Vietnam, Cambodia, and Kenya) with other common gridded population data production methodologies. We discuss the advantages of the new method and increases over the accuracy and flexibility of those previous approaches. Finally, we outline how this algorithm will be extended to provide freely-available gridded population data sets for Africa, Asia and Latin America.
Subfields: Global and Planetary Change, Transportation
View Paper: https://www.semanticscholar.org/paper/9d9cf3886493e7aa26c8e69d5947f3e3fad70485
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Global and Planetary Change
In Transportation
- Andrew J. Tatem
- Andrea E. Gaughan
- Forrest R. Stevens
- Todd M. Bacastow
- Adam Van Etten
- David Lindenbaum
- Catherine Linard
Citations in the corpus
- SpaceNet: A Remote Sensing Dataset and Challenge Series (2018, background): “useful proxy for population density [2], [3], [4].”
- The spatial allocation of population: a review of large-scale gridded population data products and their fitness for use (2019, methodology): “Individual country outputs from the WorldPop project provide an open access, transparently documented archive of spatial demographic datasets for many regions in the world including Central and South America, Africa and Asia to support development, disaster response and health applications (Gaughan ”
- Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling (2019, methodology): “Due to the highly skewed distribution of the population density, we use a log transformation as frequently done in population prediction studies (Linard et al. 2013; Stevens et al. 2015).”